In this section, we present result on Tiny ImageNet dataset, a similar kind of image classification database like the ImageNet Large Scale Visual Recognition Challenge(ILSVRC). Tiny Imagenet contains \(64 \times 64\)  RGB images with total 100,000 training images, 10,000 validation images, and 10,000 test images and have total 200 image classes. We report a mean of 5 different runs for Top-1 accuracy in table REF  on WideResNet 28-10 (WRN 28-10) [1]} model. We consider a batch size of 32, He Normal initializer [2]}, 0.2 dropout rate [3]}, adam optimizer [4]}, initial learning rate(lr rate) 0.01, and lr rate is reduced by a factor of 10 after every 60 epochs up-to 300 epochs. We have applied standard data augmentation methods like rotation, width shift, height shift, shearing, zoom, horizontal flip, fill mode. It is evident from the table that the proposed function performs better than the baseline functions, and top-1 accuracy is stable (mean\(\pm \) std) and got a good improvement for SAU over ReLU.
<TABLE>